Mar 9, 2021 · We propose a novel feature embedding module that derives from canonical correlation analyses to account for intra-modality and inter-modality correlations.

This work proposes a novel deep learning approach for survival risk stratification by integrating histopathological imaging, genetic and clinical data.

These em- beddings perform competitively on one-year survival clas- sification of TCGA-BRCA breast cancer patients, yielding average F1 scores up to 58.69% ...

Dive into the research topics of 'Multimodal fusion using sparse cca for breast cancer survival prediction'. Together they form a unique fingerprint.

Mar 9, 2021 · The availability of multi-modality datasets provides a unique opportunity to characterize the same object of interest using multiple viewpoints ...

The exploitation of relations within each modality has been successfully introduced in cancer prognosis via bilinear model (Wang et al., 2021b) and graph-based ...

Multimodal fusion using sparse CCA for breast cancer survival prediction ; dc.contributor.author, Subramanian, Vaishnavi ; dc.contributor.author, Syeda-Mahmood, ...

Code accompanying our ISBI 2021 paper "Multimodal Fusion using Sparse CCA for Breast Cancer ... Survival prediction with K-dimensional CCA on BRCA data.

This work investigates the use of canonical correlation analysis (CCA) and penalized variants of CCA (pCCA), and proposes a two-stage prediction pipeline ...

In this study, we propose a novel cancer survival prediction method HFBSurv via hierarchical factorized bilinear fusion. To obtain comprehensive multimodal ...